# Current Focus: Report Generation Module Implementation (Phase 4)

## Latest Update (2025-02-27)

We have successfully implemented Phases 1, 2, and 3 of the Report Generation module. The next focus is on Phase 4: Advanced Features, which includes support for alternative models, progressive report generation, visualization components, and interactive elements.

### Recent Progress

1. **Report Generation Module Phase 3 Implementation**:
   - Integrated with Groq's Llama 3.3 70B Versatile model for report synthesis
   - Implemented a map-reduce approach for processing document chunks:
     - Map: Process individual chunks to extract key information
     - Reduce: Synthesize extracted information into a coherent report
   - Created report templates for different query types (factual, exploratory, comparative)
   - Added citation generation and reference management
   - Implemented Markdown formatting for reports
   - Created comprehensive test scripts to verify functionality

2. **LLM Integration Enhancements**:
   - Created a dedicated ReportSynthesizer class for report generation
   - Configured proper integration with Groq and OpenRouter providers
   - Implemented error handling and logging throughout the process
   - Added support for different query types with automatic detection

3. **Testing Framework Updates**:
   - Created a dedicated test script for the report synthesis functionality
   - Implemented tests with both sample data and real URLs
   - Added support for mock data to avoid API dependencies during testing
   - Verified end-to-end functionality from document scraping to report generation

### Current Tasks

1. **Report Generation Module Implementation (Phase 4)**:
   - Adding support for alternative models with larger context windows
   - Implementing progressive report generation for very large research tasks
   - Creating visualization components for data mentioned in reports
   - Adding interactive elements to the generated reports
   - Implementing report versioning and comparison
   - Implementing customizable report detail levels

2. **Integration with UI**:
   - Adding report generation options to the UI
   - Implementing progress indicators for document scraping and report generation
   - Creating visualization components for generated reports
   - Adding options to customize report generation parameters

3. **Performance Optimization**:
   - Optimizing token usage for more efficient LLM utilization
   - Implementing caching strategies for report templates and common queries
   - Enhancing parallel processing for the map phase of report generation
   - Improving error recovery and retry mechanisms

### Next Steps

1. **Complete Phase 4 of Report Generation Module**:
   - Implement support for alternative models with larger context windows
   - Develop progressive report generation for very large research tasks
   - Create visualization components for data mentioned in reports
   - Add interactive elements to the generated reports
   - Implement report versioning and comparison
   - Implement customizable report detail levels with the following options:
     - Adjustable number of search results
     - Configurable token budget
     - Customizable synthesis prompts
     - Different report style templates
     - Adjustable chunking parameters
     - Model selection options

2. **Enhance UI Integration**:
   - Add report generation options to the UI
   - Implement progress indicators for document scraping and report generation
   - Create visualization components for generated reports
   - Add options to customize report generation parameters

3. **Comprehensive Testing and Documentation**:
   - Create end-to-end tests for the complete pipeline
   - Test with various document types and sizes
   - Evaluate performance and optimize as needed
   - Create comprehensive documentation for the report generation module

### Technical Notes

- Using Groq's Llama 3.3 70B Versatile model for report synthesis
- Implemented map-reduce approach for processing document chunks
- Created report templates for different query types (factual, exploratory, comparative)
- Added citation generation and reference management
- Using asynchronous processing for improved performance in report generation
- Managing API keys securely through environment variables and configuration files